Researchers led by Anqi Liu, Jianing Liu and Qing Wu report that adding a genome-wide polygenic score—derived not from bone density measurements, but directly from actual fracture outcomes—produced a modest yet statistically significant improvement in FRAX’s ability to identify which women would suffer a major fracture within a decade. The findings, drawn from more than 10,000 participants in the Women’s Health Initiative, offer both a proof of concept for genetically enhanced fracture prediction and a sobering reminder of how difficult it is to move the needle beyond well-established clinical risk factors.

The central innovation of the study lies in where the genetic signal comes from. Previous attempts to graft genetics onto FRAX have relied on polygenic scores built from genome-wide association studies of bone mineral density, or its heel-based surrogate, estimated BMD. That approach captures genetic susceptibility only indirectly, through a trait that is correlated with fracture but not identical to it. The genetic architectures of bone density and fracture overlap, but only partially—a distinction that matters, because a woman can fracture a wrist without ever having crossed the diagnostic threshold for low bone density.

To capture the fracture-specific component of genetic risk, the team turned to freshly released summary statistics from a UK Biobank genome-wide association study of forearm fractures, comprising more than one million genetic variants. Forearm fracture was a deliberate choice: it is one of the four clinical events that define a major osteoporotic fracture, and a prior wrist fracture is among the strongest predictors of future osteoporotic breaks. From this discovery dataset, the researchers constructed two genome-wide polygenic scores using Bayesian shrinkage methods—polygenic risk score continuous shrinkage, known as PRS-CS, and its more elaborate cousin, SBayesRC.

The statistical machinery behind these scores is worth unpacking. Traditional polygenic score construction, often called clumping and thresholding, selects a sparse subset of variants by statistical significance and can mishandle linkage disequilibrium—the non-random co-inheritance of neighboring variants across the genome. Bayesian shrinkage methods take a different route. PRS-CS places a continuous shrinkage prior on every SNP’s effect size, combining a global shrinkage parameter with locus-specific parameters, and infers posterior effect estimates through Gibbs sampling while borrowing a European-ancestry linkage disequilibrium reference panel from the UK Biobank. SBayesRC extends this framework with an annotation-modulated mixture prior, allowing the expected magnitude of each variant’s effect to depend on functional genomic annotations—essentially letting the model down-weight variants in genomic regions unlikely to matter and up-weight those in functionally significant territory. Both methods are fully Bayesian and tuning-free, requiring no manual selection of hyperparameters, which the authors note improves reproducibility and reduces analyst degrees of freedom.

With the genetic scores in hand, the researchers built what they call GPS-FRAX models. The baseline comparator was FRAX computed from clinical risk factors alone—age, body mass index, prior fracture, parental history of hip fracture, smoking, alcohol use, glucocorticoid exposure, rheumatoid arthritis and secondary causes of osteoporosis—without any bone density input. Because FRAX outputs a bounded probability between zero and one, the team logit-transformed it before inserting it into a Fine-Gray subdistribution hazard model, a survival model specifically designed to handle competing risks. This mattered here: 218 women in the cohort died before sustaining any fracture, and ignoring those deaths would have inflated apparent fracture risk. The models also retained age as an independent covariate and, crucially, included a genetic-score-by-age interaction term, allowing the weight of inherited risk to shift across the lifespan—an acknowledgment that genetic susceptibility to bone loss may express itself differently in a 55-year-old than in a 75-year-old. The first ten principal components of genetic ancestry were included as covariates to guard against confounding by population stratification.

The validation population consisted of 10,135 postmenopausal women drawn from several Women’s Health Initiative genomic sub-studies, followed for an average of 16.4 years and analyzed over a 10-year horizon aligned with FRAX’s framework. During that window, 765 women—7.55 percent of the cohort—suffered a major osteoporotic fracture of the hip, spine, wrist or proximal humerus. The cohort’s mean age was 64.3 years, and women who fractured were, unsurprisingly, older on average than those who did not.

So how much did genetics add? Measured by the workhorse metric of predictive discrimination—the time-dependent area under the receiver operating characteristic curve at ten years—the gains were small but consistent. The clinical-risk-factor-only FRAX model achieved an AUC of 0.683. Adding the PRS-CS-derived genetic score pushed that to 0.693; the SBayesRC version reached 0.690. In reclassification analysis using the fixed 20 percent treatment threshold endorsed by the U.S. National Osteoporosis Foundation, only 1.58 to 1.82 percent of women changed risk categories when genetics were added. Yet the net reclassification improvement—a metric that weighs correctly moved individuals against incorrectly moved ones—was significantly positive: 2.20 percent for SBayesRC and 2.72 percent for PRS-CS, with confidence intervals comfortably excluding zero. Decision curve analysis, which quantifies net clinical benefit across a range of risk thresholds from zero to 25 percent, likewise suggested the genetic models delivered incremental value in clinically relevant threshold ranges.

The authors are careful about what these numbers do and do not mean. An AUC shift of one percentage point will not rewrite screening guidelines overnight, and the study’s own conclusions emphasize that the added predictive value beyond established clinical risk factors remains modest. The women in the cohort were overwhelmingly of European ancestry, and polygenic scores are notorious for poor portability across ancestries—a limitation the researchers flag directly, calling for evaluation in more diverse populations. There is also a subtle statistical wrinkle: because the coefficient of the FRAX probability was re-estimated within the Women’s Health Initiative cohort rather than fixed at its conventional value, the GPS-FRAX models are best understood as cohort-recalibrated FRAX models augmented with genetics, not as a drop-in replacement for the clinical calculator itself. A sensitivity analysis using offset-based models, with the FRAX coefficient fixed at one, was conducted to probe this distinction.

Even so, the study’s framing represents a meaningful conceptual shift in the field. By building polygenic scores from fracture itself rather than from bone density proxies, the researchers tested a more direct biological hypothesis: that the genetics of actually breaking a bone includes information—about bone quality, geometry, fall mechanics, and perhaps traits not yet measured—that density-based scores leave on the table. The forearm fracture GWAS summary statistics made that test feasible at scale for the first time, and the answer, at least in this population, is that the direct signal adds something real, if small, on top of the clinical model. A subgroup analysis among 689 women with available DXA bone density measurements further compared clinical-FRAX, FRAX-with-BMD, FRAX-with-BMD-plus-genetics, and genetics-plus-BMD-plus-age models, probing whether the genetic contribution survives once imaging data enter the picture.

The research, conducted under Institutional Review Board approval at Ohio State University using data accessed through the Database of Genotypes and Phenotypes, lands at a moment of genuine ferment in osteoporosis prediction. Related work published this year has shown that Bayesian bone-density-derived polygenic scores can also enhance FRAX, and earlier studies have demonstrated incremental gains from genome-wide scores built on associated SNPs. The accumulating evidence sketches a likely trajectory: genetic risk scoring will not replace clinical calculators, but it may progressively slot into them, particularly for the large group of postmenopausal women sitting in the ambiguous “gray zone” of intermediate risk, where the decision to treat or wait remains genuinely uncertain. For those women, even a small, statistically robust improvement in the precision of a 10-year forecast could change what gets prescribed—and, potentially, which fractures never happen. The next test, the authors make clear, is whether the same forensic use of fracture genetics holds up in populations whose genomes the discovery data never saw.

Rizzoli is an internist and endocrinologist whose career has focused on metabolic bone diseases, osteoporosis, and disorders of mineral metabolism. He is Emeritus Professor of Medicine at the University Hospitals of Geneva, where he previously headed the Service of Bone Diseases and chaired the Department of Rehabilitation and Geriatrics. His clinical and research background spans the complex biological systems that regulate skeletal strength, including calcium and phosphate balance, bone remodeling, endocrine signaling, and the effects of aging on musculoskeletal function. These areas are central to modern osteoporosis medicine because skeletal fragility is not determined by bone mineral density alone. Bone architecture, cortical porosity, turnover rate, previous fractures, falls, muscle function, and exposure to medications can all influence a patient’s risk.

The new editor-in-chief brings extensive experience in scientific publishing. He previously served as Editor-in-Chief of Calcified Tissue International, was an editor of BONE, and has worked as an Associate Editor of Osteoporosis International. He also chaired the IOF Committee of Scientific Advisors and currently chairs the Scientific Advisory Board of the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases, known as ESCEO. Such roles place Rizzoli at the intersection of research evaluation, clinical practice, and health policy. Editorial leadership in this field requires more than selecting technically sound manuscripts; it also involves identifying studies that can change clinical decisions, clarify uncertainty, and improve prevention strategies for populations at risk of fracture.

In his new role, Rizzoli will work with the US Editor-in-Chief, who is appointed by the Bone Health and Osteoporosis Foundation, as well as the journals’ editorial boards, authors, reviewers, and IOF. The joint structure reflects the international nature of osteoporosis research. Although the disease is often associated with older women, osteoporosis affects people of all sexes and ethnic backgrounds, and its causes and consequences vary across regions. Hip, vertebral, and wrist fractures can lead to chronic pain, reduced mobility, loss of independence, and increased mortality. The burden is expected to grow as populations age, making international collaboration essential for comparing fracture rates, evaluating screening strategies, and adapting treatments to different healthcare systems.

“Osteoporosis International” publishes research across the spectrum of bone disease, including original investigations, reviews, educational articles, and case reports. Its scientific scope ranges from clinical trials and observational studies to laboratory research that has direct relevance to human disease. This breadth is important because osteoporosis is produced by a dynamic imbalance between bone resorption and bone formation. Specialized cells called osteoclasts break down old bone, while osteoblasts create new tissue. In healthy adult bone, these processes are tightly coupled. With aging, hormonal changes, chronic inflammation, nutritional deficiencies, certain cancers, and some medications, resorption may outpace formation, gradually weakening the skeleton and increasing the likelihood that an ordinary fall will cause a fracture.

The journals also provide a platform for research into how osteoporosis is diagnosed and treated. Dual-energy X-ray absorptiometry, or DXA, remains the standard method for measuring bone mineral density at the hip and spine, but clinicians increasingly combine DXA results with clinical risk factors. Tools such as FRAX estimate the probability of a major osteoporotic fracture or hip fracture over a defined period by incorporating age, sex, previous fracture, family history, glucocorticoid use, smoking, alcohol intake, rheumatoid arthritis, and other variables. Researchers are also investigating vertebral fracture assessment, high-resolution imaging, bone turnover markers, artificial intelligence, and genetic risk scores. These approaches may help identify individuals whose bone structure is fragile even when their bone density does not fall below conventional diagnostic thresholds.

Treatment research is equally active. Established antiresorptive medicines, including bisphosphonates and denosumab, reduce bone breakdown and can lower fracture risk, while anabolic and bone-forming therapies stimulate new bone formation in selected patients at very high risk. However, the most appropriate treatment depends on fracture history, kidney function, age, other illnesses, medication access, and the potential risks associated with stopping or changing therapy. Long-term management can be challenging because osteoporosis is often silent until a fracture occurs. Patients may discontinue treatment when they feel well, while clinicians must balance the benefits of fracture prevention against uncommon but serious adverse events. Rigorous evidence and clear clinical guidance are therefore essential, particularly as newer therapies and treatment sequences enter routine practice.

Rizzoli said he was honored to take on the position and expressed his intention to build on the journal’s reputation as a leading publication in osteoporosis and metabolic bone diseases. He emphasized the importance of rigorous, impactful, peer-reviewed research that can advance scientific knowledge, inform clinical practice, and improve musculoskeletal health worldwide. That mission is increasingly urgent because osteoporosis intersects with several of the largest health challenges of the twenty-first century, including population aging, multimorbidity, disability, healthcare inequality, and the rising cost of long-term care. Preventing a fracture can preserve mobility and independence, but doing so often requires earlier identification of risk and coordinated intervention involving primary care, endocrinology, geriatrics, rehabilitation, radiology, nursing, and public health.

The appointment also closes an 18-year editorial chapter under Kanis, whose leadership helped strengthen the international profile of Osteoporosis International. During his tenure, the journal attracted research from around the world and reinforced its position as one of the leading publications in osteoporosis and metabolic bone disease. IOF President Professor Nicholas Harvey credited Kanis with helping shape the journal’s scientific standing and establishing a strong foundation for its future. The transition reflects the continuity of a publication while acknowledging changes in the science itself: osteoporosis research now extends from molecular mechanisms and drug development to fracture liaison services, fall prevention, digital health, health economics, and strategies designed to close treatment gaps after a first fracture.

The IOF, described as the world’s largest nongovernmental organization dedicated to the prevention, diagnosis, and treatment of osteoporosis and related musculoskeletal diseases, represents scientific, medical, research, and patient organizations in 152 countries. Its leadership argues that fracture prevention and healthy mobility should become global healthcare priorities. With Rizzoli scheduled to begin his editorial term in 2026, the European Office will enter its next phase amid rapid advances in bone biology, precision medicine, and population health. The central test for the journals will be to ensure that emerging discoveries are evaluated with methodological rigor and translated into evidence that can help clinicians prevent fractures, preserve mobility, and reduce the worldwide consequences of skeletal fragility.

Subject of Research: Appointment of Professor René Rizzoli as Editor-in-Chief for the European Office of Osteoporosis International and Archives of Osteoporosis.

Article Title: René Rizzoli Appointed to Lead European Editions of Major Osteoporosis Journals

Web References: International Osteoporosis Foundation, https://www.osteoporosis.foundation/

Keywords: Osteoporosis, bone health, metabolic bone disease, osteoporosis research, René Rizzoli, Osteoporosis International, Archives of Osteoporosis, scientific publishing, fracture prevention, bone mineral density, musculoskeletal health, International Osteoporosis Foundation

The primary focus of FREM_ML is to utilize a comprehensive database that encompasses a wide array of clinical indicators, lifestyle factors, and demographic data. Traditional fracture risk assessments often rely on subjective interpretations of data or singular metrics such as bone mineral density, which can overlook critical factors influencing a patient’s overall risk. By employing machine learning algorithms, FREM_ML identifies patterns and correlations across diverse datasets, ensuring a more holistic understanding of each patient’s situation.

Central to the effectiveness of FREM_ML is its ability to process vast amounts of information far more rapidly and accurately than human practitioners could manage. Utilizing a blend of historical patient outcomes, genetic predispositions, and environmental influences, the algorithm can generate a risk profile for individual patients quickly. This rapid assessment allows for timely interventions that can significantly mitigate the potential for fractures, which can lead to serious complications, including disability and even mortality in older adults.

The development and deployment of FREM_ML are underscored by the urgent need for healthcare systems worldwide to transition to more data-driven models. The old paradigms of one-size-fits-all assessment tools have proven inadequate when addressing the unique complexities of fracture risk. FREM_ML not only enhances the precision of risk assessments but also empowers clinicians with actionable insights, equipping them to devise personalized prevention strategies tailored to individual patient profiles.

One of the most notable aspects of FREM_ML is its user-friendly interface. This design consideration ensures that healthcare providers, regardless of their technical expertise, can easily navigate the system to obtain crucial insights into fracture risks. With intuitive visualizations and recommendations, clinicians can make informed decisions that align with the latest clinical guidelines, further bridging the gap between technology and healthcare practice.

Moreover, FREM_ML addresses a critical issue in healthcare: the management of resource allocation. By identifying high-risk individuals accurately, healthcare systems can focus their efforts on preventive measures for those who need it most. This targeted approach not only enhances patient outcomes but also optimizes the utilization of medical resources, thereby reducing costs associated with managing fractures after they occur.

As the study highlights, the successful implementation of FREM_ML depends on collaboration between data scientists, healthcare providers, and policymakers. Creating a seamless integration of this technology within existing healthcare infrastructures requires a concerted effort from all stakeholders. The promise of improved patient outcomes creates a compelling case for this collaborative approach, with potential benefits extending into broader public health domains.

Importantly, the potential for FREM_ML to adapt and evolve is immense. Future iterations of the system could incorporate ongoing advancements in genomics and personalized medicine, ensuring that the technology remains at the forefront of fracture risk assessment. This adaptability aligns with trends in healthcare highlighting the significance of tailored treatment plans, shifting the focus from reactive to proactive health management.

In an era marked by technological innovation, it is crucial that the medical community embraces tools like FREM_ML. The intersection of artificial intelligence and medicine presents endless possibilities, and FREM_ML exemplifies how these advancements can lead to better health outcomes. As more researchers and institutions explore similar paradigms, the collective knowledge gained could foster an environment where personalized medicine thrives, ultimately benefiting a greater number of patients.

The implications of FREM_ML are not confined solely to fracture risk assessment. The fundamentally new approach it proposes could reshape how we think about chronic disease management as a whole. By establishing robust methodologies for risk prediction across various medical domains, FREM_ML sets a precedent that other areas of healthcare can learn from, potentially leading to improvements in treatment efficiency and patient care.

In conclusion, FREM_ML represents more than just an advanced tool for fracture risk assessment; it embodies a shift towards a more integrated and data-driven philosophy in medicine. As further research unfolds and the technology matures, its potential to influence strategies for injury prevention, especially among vulnerable populations, is both promising and revolutionary. The future of fracture risk management looks bright, thanks to the initiative led by Rietz and colleagues.

Achieving widespread adoption of FREM_ML will necessitate continuous evaluation and refinement. Future studies will undoubtedly play a vital role in assessing the efficacy of the model in real-world settings and its adaptability to diverse healthcare environments. With its promising inception, FREM_ML holds the possibility of becoming a gold standard in identifying and mitigating fracture risk, significantly impacting how healthcare professionals approach osteoporosis management.

As we move forward, maintaining an informed dialogue among healthcare practitioners, patients, and researchers will be essential in harnessing the full potential of FREM_ML and similar innovations. This collaborative effort will not only optimize the model itself but also enhance our understanding of fracture risks associated with aging and osteoporotic conditions. Ultimately, it is the collective aim of the medical community to create a healthier, more resilient population capable of living longer, fracture-free lives.

Subject of Research: Automated identification of individuals at high imminent fracture risk

Article Title: Introducing FREM_ML: a decision-support approach for automated identification of individuals at high imminent fracture risk

Article References:

Rietz, M., Brønd, J.C., Möller, S. et al. Introducing FREM_ML: a decision-support approach for automated identification of individuals at high imminent fracture risk.
Arch Osteoporos 20, 140 (2025). https://doi.org/10.1007/s11657-025-01613-5

Image Credits: AI Generated

DOI: https://doi.org/10.1007/s11657-025-01613-5

Keywords: Fracture risk, FREM_ML, machine learning, osteoporosis, healthcare innovation.